Modeling Spectral Variability for the Classification of Depressed Speech
Nicholas Cummins
NICTA SML SEMINARDATE: 2013-06-20
TIME: 11:15:00 - 12:15:00
LOCATION: NICTA - 7 London Circuit
CONTACT: JavaScript must be enabled to display this email address.
ABSTRACT:
Quantifying how the spectral content of speech relates to changes in mental state may be crucial in building an objective speech-based depression classification system with clinical utility. This paper investigates the hypothesis that important depression based information can be captured within the covariance structure of a Gaussian Mixture Model (GMM) of recorded speech. Significant negative correlations found between a speakeras average weighted variance - a GMM-based indicator of speaker variability - and their level of depression support this hypothesis. Further evidence is provided by the comparison of classification accuracies from seven different GMM-UBM systems, each formed by varying different parameter combinations during MAP adaption. This analysis shows that variance-only adaptation either outperforms or matches the de facto standard mean-only adaptation when classifying both the presence and severity of depression. This result is perhaps the first of its kind seen in GMM-UBM speech classification.
